Machine Learning in Healthcare: Regulatory Requirements, Reimbursement Challenges, Privacy and Security Risks

Next Week
Webinar (ET)

Alaap B. Shah, Member of the Firm, will participate in the panel discussion "Machine Learning in Healthcare: Regulatory Requirements, Reimbursement Challenges, Privacy and Security Risks," a webinar hosted by Strafford.

This CLE webinar will guide healthcare counsel on machine learning in the healthcare context. The panel will discuss how healthcare companies and providers are using machine learning to provide healthcare, patient care, and administrative processes. The panel will examine the regulatory requirements and the implications for reimbursement. The panel will also address privacy and security issues and offer best practices for compliance when using machine learning in healthcare.

Machine learning has virtually unlimited uses in the healthcare industry. From pacemakers to smart scalpels to smartwatches to radiology and detecting cancers to mapping infectious disease, healthcare providers can leverage machine learning to provide better and better healthcare. Machine learning can also be used to streamline administrative processes in hospitals.

There are legal issues that are raised with the use of machine learning in healthcare. Among the legal concerns are the regulatory requirements, reimbursement issues, privacy and security issues, and standard of care. For example, machine learning presents challenges to companies with obligations to safeguard protected health information and other sensitive information. Further, the use of machine learning may implicate HIPAA as well as state privacy and security laws. Machine learning presents risks of privacy breaches and cybersecurity threats.

It is critical for healthcare organizations, providers, and counsel to recognize how machine learning impacts the provision of care and address the legal implications.

Learning objectives:

  • How can healthcare providers minimize liability risks when using machine learning for patient care - or when deciding not to use it?
  • Who may be liable when a healthcare provider's care is based on machine learning?

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